Technical Papers
Jul 19, 2019

Annual Average Daily Traffic Prediction Model for Minor Roads at Intersections

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 10

Abstract

Annual average daily traffic (AADT) is an important element for maintenance, safety, environmental analysis, finance, engineering economics, and performance management. Most previous studies were conducted to estimate AADT on the road segment instead of considering the intersection, and did not well consider the possible difference of AADT between the major road and minor road at intersections. The present research was conducted to develop a model that can estimate AADT for minor roads at intersections using available Highway Performance Monitoring System (HPMS) data owned by state departments of transportation and Census data. The performance of multiple linear regression, random forest, and neural network were compared in the study. Multiple regression analysis was selected to develop an estimation function for the minor road AADT. The AADT on the major road, the functional class of the major road and minor road, and the number of traffic lanes on the major road and minor road were selected as the input of the regression model, which was based on the statistical analysis. A multiple regression model with logarithmic transmission was selected for AADT estimation. The cross-validation showed the high accuracy of the developed model. The equation generated in this paper can be easily used by transportation agencies for AADT estimation on minor roads at intersections.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

This research was supported by the Nevada Department of Transportation Traffic Safety Engineering (NDOT TSE). The authors acknowledge the Iowa State University Center for Transportation Research and Education (CTRE) for providing the SHRP 2 RID database. The authors thank Chuck Reider, former Chief Safety Engineer at NDOT, for his valuable comments.

References

AASHTO. 1992. AASHTO guidelines for traffic data programs. Washington, DC: AASHTO.
Castro-Neto, M., Y. Jeong, M. K. Jeong, and L. D. Han. 2009. “AADT prediction using support vector regression with data-dependent parameters.” Expert Syst. Appl. 36 (2): 2979–2986. https://doi.org/10.1016/j.eswa.2008.01.073.
Jiang, Z., M. R. McCord, and P. K. Goel. 2006. “Improved AADT estimation by combining information in image-and ground-based traffic data.” J. Transp. Eng. 132 (7): 523–530. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:7(523).
Lam, W. H., and J. Xu. 2000. “Estimation of AADT from short period counts in Hong Kong—A comparison between neural network method and regression analysis.” J. Adv. Transp. 34 (2): 249–268. https://doi.org/10.1002/atr.5670340205.
Lingras, P., and M. Adamo. 1995. “Estimation of AADT volumes using neural networks.” In Proc., Computing in Civil and Building, edited by P. J. Pahl and H. Wemer, 1355–1362. Rotterdam, Netherlands: A.A. Balkema.
Lord, D., and P. Y. J. Park. 2008. “Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates.” Accid. Anal. Prev. 40 (4): 1441–1457. https://doi.org/10.1016/j.aap.2008.03.014.
McLaughlin, S. B., and J. M. Hankey. 2015. Naturalistic driving study: Linking the study data to the roadway information database. Washington, DC: Transportation Research Board.
Mohamad, D., K. Sinha, T. Kuczek, and C. Scholer. 1998. “Annual average daily traffic prediction model for county roads.” Transp. Res. Rec. 1617 (1): 69–77. https://doi.org/10.3141/1617-10.
O’Brien, R. M. 2007. “A caution regarding rules of thumb for variance inflation factors.” Qual. Quantity 41 (5): 673–690. https://doi.org/10.1007/s11135-006-9018-6.
Pande, A., A. Das, M. Abdel-Aty, and H. Hassan. 2011. “Estimation of real-time crash risk: Are all freeways created equal?” Transp. Res. Rec. 2237 (1): 60–66. https://doi.org/10.3141/2237-07.
Persaud, B., R. Retting, P. Garder, and D. Lord. 2001. “Safety effect of roundabout conversions in the United States: Empirical Bayes observational before-after study.” Transp. Res. Rec. 1751 (1): 1–8. https://doi.org/10.3141/1751-01.
Raja, P., M. Doustmohammadi, and M. D. Anderson. 2018. “Estimation of average daily traffic on low volume roads in Alabama.” In Proc., 97th Annual Meeting on Transportation Research Board. Washington, DC: Transportation Research Board.
Sharma, S. C., B. M. Gulati, and S. N. Rizak. 1996. “Statewide traffic volume studies and precision of AADT estimates.” J. Transp. Eng. 122 (6): 430–439. https://doi.org/10.1061/(ASCE)0733-947X(1996)122:6(430).
Sharma, S. C., P. Lingras, F. Xu, and P. Kilburn. 2001. “Application of neural networks to estimate AADT on low-volume roads.” J. Transp. Eng. 127 (5): 426–432. https://doi.org/10.1061/(ASCE)0733-947X(2001)127:5(426).
Sharma, S. C., P. Lingras, F. Xu, and G. Liu. 1999. “Neural networks as alternative to traditional factor approach of annual average daily traffic estimation from traffic counts.” Transp. Res. Rec. 1660 (1): 24–31. https://doi.org/10.3141/1660-04.
Sun, Y., H. Xu, J. Wu, E. Y. Hajj, and X. Geng. 2017. “Data processing framework for development of driving cycles with data from SHRP 2 naturalistic driving study.” Transp. Res. Rec. 2645 (1): 50–56. https://doi.org/10.3141/2645-06.
Unnikrishnan, A., M. Figliozzi, M. K. Moughari, and S. Urbina. 2018. A method to estimate annual average daily traffic for minor facilities for MAP-21 reporting and statewide safety analysis. Washington, DC: Federal Highway Administration.
US Census Bureau. 2019. “Geography program.” Accessed January 20, 2019. https://www.census.gov/geo/maps-data/data/tiger.html.
Wu, J. 2017. Effect and influence of different factors on driver behavior when vehicles make right turns at signalized intersections. Washington, DC: Transportation Research Board.
Wu, J., and H. Xu. 2017. “Driver behavior analysis for right-turn drivers at signalized intersections using SHRP 2 naturalistic driving study data.” J. Saf. Res. 63 (Dec): 177–185. https://doi.org/10.1016/j.jsr.2017.10.010.
Wu, J., and H. Xu. 2018. “The influence of road familiarity on distracted driving activities and driving operation using naturalistic driving study data.” Transp. Res. Part F: Traffic Psychol. Behav. 52 (Jan): 75–85. https://doi.org/10.1016/j.trf.2017.11.018.
Wu, J., H. Xu, Y. Sun, and X. Geng. 2017. “Effect of road characteristics and driving cycles on accident risk on full-access-control highways.” In Proc., 96th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Wu, J., H. Xu, and J. Zhao. 2018a. “Automatic lane identification using the roadside LiDAR sensors.” IEEE Intell. Transp. Syst. Mag. PP (99): 1. https://doi.org/10.1109/MITS.2018.2876559.
Wu, J., H. Xu, Y. Zheng, W. Liu, Y. Sun, R. Yue, and X. Song. 2018b. “Driver behavior fault analysis on ramp-related crashes/near-crashes using SHRP 2 naturalistic driving study data.” In Proc., 21th IEEE Intelligent Transportation Systems Conf. Piscataway, NJ: Institute of Electrical and Electronics Engineers.
Xia, Q., F. Zhao, Z. Chen, L. D. Shen, and D. Ospina. 1999. “Development of a regression model for estimating AADT in a Florida county.” Transp. Res. Rec. 1660 (1): 32–40. https://doi.org/10.3141/1660-05.
Xie, F., K. Gladhill, K. Dixon, and C. Monsere. 2011. “Calibration of highway safety manual predictive models for Oregon state highways.” Transp. Res. Rec. 2241 (1): 19–28. https://doi.org/10.3141/2241-03.
Xu, H., and J. Wu. 2018. “Use of naturalistic driving study data to determine right-turn driver deceleration behavior at signalized intersections.” In Proc., 97th Annual Meeting on Transportation Research Board. Washington, DC: Transportation Research Board.
Zhao, F., and S. Chung. 2001. “Contributing factors of annual average daily traffic in a Florida county: Exploration with geographic information system and regression models.” Transp. Res. Rec. 1769 (1): 113–122. https://doi.org/10.3141/1769-14.
Zhao, F., and N. Park. 2004. “Using geographically weighted regression models to estimate annual average daily traffic.” Transp. Res. Rec. 1879 (1): 99–107. https://doi.org/10.3141/1879-12.
Zhao, J., H. Xu, D. Wu, and H. Liu. 2018. “An artificial neural network to identify pedestrians and vehicles from roadside LiDAR data.” In Proc., 97th Annual Meeting on Transportation Research Board. Washington, DC: Transportation Research Board.
Zheng, J., and H. X. Liu. 2017. “Estimating traffic volumes for signalized intersections using connected vehicle data.” Transp. Res. Part C: Emerging Technol. 79 (Jun): 347–362. https://doi.org/10.1016/j.trc.2017.03.007.

Information & Authors

Information

Published In

Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 145Issue 10October 2019

History

Received: Dec 19, 2017
Accepted: Feb 4, 2019
Published online: Jul 19, 2019
Published in print: Oct 1, 2019
Discussion open until: Dec 19, 2019

Permissions

Request permissions for this article.

Authors

Affiliations

Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Nevada, Reno, 1664 N. Virginia St., MS258, Reno, NV 89557. ORCID: https://orcid.org/0000-0003-2844-6082. Email: [email protected]
Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Nevada, Reno, 1664 N Virginia St., MS258, Reno, NV 89557 (corresponding author). Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share